Validation of a Reduced Set of High-Performance Triggers for Identifying Patient Safety Incidents with Harm in Primary Care: TriggerPrim Project.

OBJECTIVE: The aim of the study was to construct and validate a reduced set of high-performance triggers for identifying adverse events (AEs) via electronic medical records (EMRs) review in primary care (PC). METHODS: This was a cross-sectional descriptive study for validating a diagnostic test. The study included all 262 PC centers of Madrid region (Spain). Patients were older than 18 years who attended their PC center over the last quarter of 2018. The randomized sample was n = 1797. Main measurements were as follows: ( a ) presence of each of 19 specific computer-identified triggers in the EMR and ( b ) occurrence of an AE. To collect data, EMR review was conducted by 3 doctor-nurse teams. Triggers with statistically significant odds ratios for identifying AEs were selected for the final set after adjusting for age and sex using logistic regression. RESULTS: The sensitivity (SS) and specificity (SP) for the selected triggers were: ≥3 appointments in a week at the PC center (SS = 32.3% [95% confidence interval {CI}, 22.8%-41.8%]; SP = 92.8% [95% CI, 91.6%-94.0%]); hospital admission (SS = 19.4% [95% CI, 11.4%-27.4%]; SP = 97.2% [95% CI, 96.4%-98.0%]); hospital emergency department visit (SS = 31.2% [95% CI, 21.8%-40.6%]; SP = 90.8% [95% CI, 89.4%-92.2%]); major opioids prescription (SS = 2.2% [95% CI, 0.0%-5.2%]; SP = 99.8% [95% CI, 99.6%-100%]); and chronic benzodiazepine treatment in patients 75 years or older (SS = 14.0% [95% CI, 6.9%-21.1%]; SP = 95.5% [95% CI, 94.5%-96.5%]).The following values were obtained in the validation of this trigger set (the occurrence of at least one of these triggers in the EMR): SS = 60.2% (95% CI, 50.2%-70.1%), SP = 80.8% (95% CI, 78.8%-82.6%), positive predictive value = 14.6% (95% CI, 11.0%-18.1%), negative predictive value = 97.4% (95% CI, 96.5%-98.2%), positive likelihood ratio = 3.13 (95% CI, 2.3-4.2), and negative likelihood ratio = 0.49 (95% CI, 0.3-0.7). CONCLUSIONS: The set containing the 5 selected triggers almost triples the efficiency of EMR review in detecting AEs. This suggests that this set is easily implementable and of great utility in risk-management practice.

Is primary care a patient-safe setting? Prevalence, severity, nature, and causes of adverse events: numerous and mostly avoidable.

Knowing the frequency and characteristics of adverse events (AEs) is key to implementing actions that can prevent their occurrence. However, reporting systems are insufficient for this purpose and epidemiological studies are also required. Currently, the reviewing of clinical records is the gold standard method for knowing the frequency and characteristics of AEs. Research on AEs in a primary care setting has been limited and primarily focuses on specific types of events (medication errors, etc.) or patients. Large studies that search for any kind of AE in all patients are scarce. This study aimed to estimate the prevalence of AEs in the primary care setting and their characteristics. SETTING: all 262 primary health-care centres in the Madrid region (Spain) during the last quarter of 2018. DESIGN: cross-sectional descriptive study. Eligible population: subjects over 18 years of age who attended medical consultation over the last year (N = 2 743 719); a randomized sample stratified by age. MAIN OUTCOMES: age, sex, occurrence of an AE, number of consultations in the study period, avoidability, severity, place of occurrence, type of event, and contributory factors. The clinical records were reviewed by three teams, each composed of one doctor and one nurse trained and with expertise in patient safety. The SPSS software package (version 26) was used for the statistical analyses. The evaluators reviewed 1797 clinical records. The prevalence of AEs over the study period was 5.0% [95% confidence interval (CI): 4.0%‒6.0%], with higher values in women (5.7%; 95% CI: 4.6%‒6.8%;P = 0.10) and patients over 75 years of age (10.3%; 95% CI: 8.9%‒11.7%; P < 0.001). The overall occurrence per hundred consultations was estimated to be 1.58% (95% CI: 1.28%‒1.94%). Of the detected AEs, 71.3% (95% CI: 62.1%‒80.5%) were avoidable. Additionally, 60.6% (95% CI: 50.7%‒70.5%) were categorized as mild, 31.9% (95% CI: 22.4%‒41.4%) as moderate, and 7.4% (95% CI: 2.1%‒12.7%) as severe. Primary care was the occurrence setting in 76.6% (95% CI: 68.0%‒85.2%) of cases. The overall incidence of AEs related to medication was 53.2% (95% CI: 50.9%‒55.5%). The most frequent types of AEs were prescription errors (28.7%; 95% CI: 19.5%‒37.9%), followed by drug administration errors by patients (17.0%; 95% CI: 9.4%‒24.6%), and clinical assessment errors (11.7%; 95% CI: 5.2%‒18.2%). The most common contributory factors were those related to the patient (80.6%; 95% CI: 71.1%‒90.1%) and tasks (59.7%; 95% CI: 48.0%‒71.4%). A high prevalence of AEs (1 in 66 consultations) was observed, which was slightly higher than that reported in similar studies. About 3 out of 4 such events were considered to be avoidable and 1 out of 13 was severe. Prescription errors, drug administration errors by patients, and clinical assessment errors were the most frequent types of AEs. Graphical Abstract.

Seguimiento de los pacientes con cardiopatía isquémica en Atención Primaria durante la pandemia por COVID-19 / Coronary heart disease follow-up in a Primary Health Care centre during the COVID-19 pandemic

Objetivo: describir el seguimiento de los factores de riesgo cardiovascular modificables (FRCM) de los pacientes con cardiopatía isquémica (CI) en un centro de salud durante el primer año la pandemia por COVID-19. Material y métodos: estudio observacional ambispectivo de un centro de salud de Madrid.Participantes: pacientes diagnosticados de CI antes de 2020, > 45 años (n: 257).Seguimiento: del 13/03/2020 al 13/03/2021.Variables: sociodemográficas, comorbilidades, análisis de sangre (AS), colesterol asociado a lipoproteínas de baja densidad (c-LDL), exploración física (tensión arterial [TA] e índice de masa corporal [IMC]), eventos cardiovasculares (ECV) y fallecimiento en el seguimiento.Se realizó en tres períodos: situación basal: registro previo al inicio de la pandemia; primer período: del 13/03/2020 al 13/09/2020; segundo período: del 14/09/2020 al 13/03/2021. Datos obtenidos de la historia clínica electrónica. Análisis: STATA16.Resultados: se hizo el seguimiento de 257 pacientes (edad: 73,14 ± 0,7; 67,3% hombres) diagnosticados de CI hace 7 años (rango intercuartílico [RIC]: 4-14). AS (al 65,6% de los pacientes se les hizo análisis en el primer período versus al 20,6% en el segundo período del estudio), TA (57,5% versus 18,6%) e IMC (29,5% versus 7,7%). Mejoraron el c-LDL hasta 2,2 mg/dL, y el IMC, 0,6 kg/m2. Empeoró la TA sistólica hasta 6,3 mmHg y la diastólica 2,5 mmHg. Un 7,7% sufrió un ECV en el primer período y un 5,8% en el segundo. Fallecieron 9 pacientes durante el seguimiento.Conclusión: casi dos tercios de los pacientes con CI habían realizado seguimiento de LDL y TA en los 6 primeros meses tras el inicio de la pandemia. El número de seguimientos caía a 1/3 al año. El IMC fue la variable menos evaluada.(AU)

Aim: to report the follow up of modifiable cardiovascular risk factors (CVRF) of patients with ischaemic heart disease in a health centre during the first year of the COVID-19 pandemic.Materials and Methods: observational follow-up study of an ambispective cohort in a Madrid health centre. Subjects: patients with CHD prior to 2020, > 45 years (n:257). Follow up: from 13/03/2020 to 13/03/2021. Variables: sociodemographic, comorbidities, blood test (BT) (LDL-c), physical examination, (blood pressure [BP] and BMI), cardiovascular events (CVE), and death during follow up. Performed over three periods: baseline period: data before the pandemic commenced. First period: 13/03/2020-13/09/2020. Second period: 14/09/2020-13/03/2021. Data collection: electronic medical record. Analysis: STATA16.Results: a total of 257 patients with CHD was followed up (age: 73.14±0.7; 67.3% male). CHD diagnosis seven years ago (IQR:4-14). BT in 65.6% of patients in the first time period vs 20.6% in the second time period, BP 57.5% vs 18.6% and BMI 29.5% vs 7.7%. LDL-c and BMI improved during follow up to 2.2 mg/dL and 0.6 kg/m2, respectively. Systolic and diastolic BP worsened up to 6.3 mmHg and 2.5 mmHg, respectively. A total of 7.7% and 5.8% suffered a CVE in the first and second time periods, respectively. In all nine patients died during follow up.Conclusion: virtually two thirds of patients with CHD had undertaken LDL-c and BP follow up in the six months since the COVID-19 pandemic started. This number decreased to one third over the year. BMI was the least evaluated variable.(AU)

SARS-CoV-2 pneumonia follow-up and long COVID in primary care: A retrospective observational study in Madrid city.

BACKGROUND: Patients with COVID-19 are follow-up in primary care and long COVID is scarcely defined. The study aim was to describe SARS-CoV-2 pneumonia and cut-offs for defining long COVID in primary care follow-up patients. METHODS: A retrospective observational study in primary care in Madrid, Spain, was conducted. Data was collected during 6 months (April to September) in 2020, during COVID-19 first wave, from patients ≥ 18 years with SARS-CoV-2 pneumonia diagnosed. Variables: sociodemographic, comorbidities, COVID-19 symptoms and complications, laboratory test and chest X-ray. Descriptive statistics were used, mean (standard deviation (SD)) and medians (interquartile range (IQR)) respectively. Differences were detected applying X2 test, Student's T-test, ANOVA, Wilcoxon-Mann-Whitney or Kruskal-Wallis depending on variable characteristics. RESULTS: 155 patients presented pneumonia in day 7.8 from the onset (79.4% were hospitalized, median length of 7.0 days (IQR: 3.0, 13.0)). After discharge, the follow-up lasted 54.0 median days (IQR 42.0, 88.0) and 12.2 mean (SD 6.4) phone calls were registered per patient. The main symptoms and their duration were: cough (41.9%, 12 days), dyspnoea (31.0%, 15 days), asthenia (26.5%, 21 days). Different cut-off points were applied for long COVID and week 4 was considered the best milestone (28.3% of the sample still had symptoms after week 4) versus week 12 (8.3%). Patients who still had symptoms >4 weeks follow-up took place over 81.0 days (IQR: 50.5, 103.0), their symptoms were more prevalent and lasted longer than those ≤ 4 weeks: cough (63.6% 30 days), dyspnoea (54.6%, 46 days), and asthenia (56.8%, 29 days). Embolism was more frequent in patients who still had symptoms >4 weeks than those with symptoms ≤4 weeks (9.1% vs 1.8%, p value 0.034). CONCLUSION: Most patients with SARS-CoV-2 pneumonia recovered during the first 4 weeks from the beginning of the infection. The cut-off point to define long COVID, as persisting symptoms, should be between 4 to 12 weeks from the onset of the symptoms.

Clinical characteristics of SARS-CoV-2 pneumonia diagnosed in a primary care practice in Madrid (Spain).

BACKGROUND: Possible cases of SARS-CoV-2 infection were diagnosed in primary care in Madrid, some of these cases had pneumonia. Most of the SARS-CoV-2 pneumonia published data came from hospitalised patients. This study set out to describe clinical characteristics of patients with SARS-CoV-2 pneumonia diagnosed in primary care across age groups and type of pneumonia. METHODS: Observational retrospective study obtaining clinical data from the electronic health records of patients who were followed-up by SARS-CoV-2 possible infection in a primary care practice in Madrid. All the cases were collected by in-person or remote consultation during the 10th March to the 7th of April. EXPOSURE: Diagnosis of SARS-CoV-2 pneumonia by chest X-ray ordered by the GP. Main outcomes and measures: Symptoms of SARS-CoV-2 pneumonia, physical examination and diagnostic tests as a blood test, nasopharyngeal swab results for RT-PCR (Reverse transcriptase-polymerase chain reaction) and chest X-ray results. RESULTS: The overall SARS-CoV-2 pneumonias collected were 172 (female 87 [50.6%], mean age 60.5 years standard deviation [SD] 17.0). Comorbidities were body mass index ≥ 25 kg/m2 (90 [52.3%]), hypertension (83 [48.3%]), dyslipidaemia (68 [39.5%]) and diabetes (33 [19.2%]). The sample was stratified by age groups (< 50 years, 50-75 years and ≥ 75 years). Clinical manifestations at onset were fever (144 [83.7%]), cough (140 [81.4%]), dyspnoea (103 [59.9%]) and gastrointestinal disturbances (72 [41.9%]). Day 7.8 (SD:4.1) from clinical onset was the mean day of pneumonia diagnosis. Bilateral pneumonia was more prevalent than unilateral (126 [73.3%] and 46 [26.7%]). Patients with unilateral pneumonia were prone to higher pulse oximetry (96% vs 94%, p < 0.001). We found differences between unilateral and bilateral cases in C-reactive protein (29.6 vs 81.5 mg/L, p < 0.001), and lymphocytes (1400.0 vs 1000.0E3/ml, p < 0.001). Complications were registered: 42 (100%) of patients ≥ 75 years were admitted into hospital; pulmonary embolism was only present at bilateral pneumonia (7 patients [5.6%]) and death occurred in 1 patient with unilateral pneumonia (2.2%) vs 10 patients (7.9%) with bilateral pneumonia ( p 0.170). CONCLUSION: Clinical manifestations of SARS-CoV-2 pneumonia were fever, cough and dyspnoea; this was especially clear in the elderly. We described different characteristics between unilateral and bilateral pneumonia.