Hospital-associated venous thromboembolism prophylaxis use by risk assessment at a large integrated health care network in Northern California.

BACKGROUND: Hospital-acquired venous thromboembolism (HA VTE) is a preventable complication in hospitalized patients. OBJECTIVE: We aimed to examine the use of pharmacologic prophylaxis (pPPX) and compare two risk assessment methods for HA VTE: a retrospective electronic Padua Score (ePaduaKP) and admitting clinician's choice of risk within the admission orderset (low, moderate, or high). DESIGN, SETTINGS AND PARTICIPANTS: We retrospectively analyzed prophylaxis orders for adult medical admissions (2013-2019) at Kaiser Permanente Northern California, excluding surgical and ICU patients. INTERVENTION: ePaduaKP was calculated for all admissions. For a subset of these admissions, clinician-assigned HA VTE risk was extracted. MAIN OUTCOME AND MEASURES: Descriptive pPPX utilization rates between ePaduaKP and clinician-assigned risk as well as concordance between ePaduaKP and clinician-assigned risk. RESULTS: Among 849,059 encounters, 82.2% were classified as low risk by ePaduaKP, with 42.3% receiving pPPX. In the subset with clinician-assigned risk (608,512 encounters), low and high ePaduaKP encounters were classified as moderate risk in 87.5% and 92.0% of encounters, respectively. Overall, 56.7% of encounters with moderate clinician-assigned risk received pPPX, compared to 7.2% of encounters with low clinician-assigned risk. pPPX use occurred in a large portion of low ePaduaKP risk encounters. Clinicians frequently assigned moderate risk to encounters at admission irrespective of their ePaduaKP risk when retrospectively examined. We hypothesize that the current orderset design may have negatively influenced clinician-assigned risk choice as well as pPPX utilization. Future work should explore optimizing pPPX for high-risk patients only.

Trends and Risk Factors for Venous Thromboembolism Among Hospitalized Medical Patients.

Importance: While hospital-associated venous thromboembolism (HA-VTE) is a known complication of hospitalization, contemporary incidence and outcomes data are scarce and methodologically contested. Objective: To define and validate an automated electronic health record (EHR)-based algorithm for retrospective detection of HA-VTE and examine contemporary HA-VTE incidence, previously reported risk factors, and outcomes. Design, Setting, and Participants: This cohort study was conducted using hospital admissions between January 1, 2013, and June 30, 2021, with follow-up until December 31, 2021. All medical (non-intensive care unit) admissions at an integrated health care delivery system with 21 hospitals in Northern California during the study period were included. Data were analyzed from January to June 2022. Exposures: Previously reported risk factors associated with HA-VTE and administration of pharmacological prophylaxis were evaluated as factors associated with HA-VTE. Main Outcomes and Measures: Yearly incidence rates and timing of HA-VTE events overall and by subtype (deep vein thrombosis, pulmonary embolism, both, or unknown), as well as readmissions and mortality rates. Results: Among 1 112 014 hospitalizations involving 529 492 patients (268 797 [50.8%] women; 75 238 Asian [14.2%], 52 697 Black [10.0%], 79 398 Hispanic [15.0%], and 307 439 non-Hispanic White [58.1%]; median [IQR] age, 67.0 [54.0-79.0] years), there were 13 843 HA-VTE events (1.2% of admissions) occurring in 10 410 patients (2.0%). HA-VTE events increased from 307 of 29 095 hospitalizations (1.1%) in the first quarter of 2013 to 551 of 33 729 hospitalizations (1.6%) in the first quarter of 2021. Among all HA-VTE events, 10 746 events (77.6%) were first noted after discharge. In multivariable analyses, several factors were associated with increased odds of HA-VTE, including active cancer (adjusted odds ratio [aOR], 1.96; 95% CI, 1.85-2.08), prior VTE (aOR, 1.71; 95% CI, 1.63-1.79), and reduced mobility (aOR, 1.63; 95% CI, 1.50-1.77). Factors associated with decreased likelihood of HA-VTE included Asian race (vs non-Hispanic White: aOR, 0.65; 95% CI, 0.61-0.69), current admission for suspected stroke (aOR, 0.73; 95% CI, 0.65-0.81), and Hispanic ethnicity (vs non-Hispanic White: aOR, 0.81; 95% CI, 0.77-0.86). HA-VTE events were associated with increased risk of readmission (hazard ratio [HR], 3.33; 95% CI, 3.25-3.41) and mortality (HR, 1.63; 95% CI, 1.57-1.70). Conclusions and Relevance: This study found that HA-VTE events occurred in 1.2% of medical admissions, increased over time, and were associated with increased adverse outcomes. These findings suggest that approaches designed to mitigate occurrence and outcomes associated with HA-VTE may remain needed.

Prognostic Accuracy of Presepsis and Intrasepsis Characteristics for Prediction of Cardiovascular Events After a Sepsis Hospitalization.

OBJECTIVES: Sepsis survivors face increased risk for cardiovascular complications; however, the contribution of intrasepsis events to cardiovascular risk profiles is unclear. SETTING: Kaiser Permanente Northern California (KPNC) and Intermountain Healthcare (IH) integrated healthcare delivery systems. SUBJECTS: Sepsis survivors (2011-2017 [KPNC] and 2018-2020 [IH]) greater than or equal to 40 years old without prior cardiovascular disease. DESIGN: Data across KPNC and IH were harmonized and grouped into presepsis (demographics, atherosclerotic cardiovascular disease scores, comorbidities) or intrasepsis factors (e.g., laboratory values, vital signs, organ support, infection source) with random split for training/internal validation datasets (75%/25%) within KPNC and IH. Models were bidirectionally, externally validated between healthcare systems. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Changes to predictive accuracy (C-statistic) of cause-specific proportional hazards models predicting 1-year cardiovascular outcomes (atherosclerotic cardiovascular disease, heart failure, and atrial fibrillation events) were compared between models that did and did not contain intrasepsis factors. Among 39,590 KPNC and 16,388 IH sepsis survivors, 3,503 (8.8%) at Kaiser Permanente (KP) and 600 (3.7%) at IH experienced a cardiovascular event within 1-year after hospital discharge, including 996 (2.5%) at KP and 192 (1.2%) IH with an atherosclerotic event first, 564 (1.4%) at KP and 117 (0.7%) IH with a heart failure event, 2,310 (5.8%) at KP and 371 (2.3%) with an atrial fibrillation event. Death within 1 year after sepsis occurred for 7,948 (20%) KP and 2,085 (12.7%) IH patients. Combined models with presepsis and intrasepsis factors had better discrimination for cardiovascular events (KPNC C-statistic 0.783 [95% CI, 0.766-0.799]; IH 0.763 [0.726-0.801]) as compared with presepsis cardiovascular risk alone (KPNC: 0.666 [0.648-0.683], IH 0.660 [0.619-0.702]) during internal validation. External validation of models across healthcare systems showed similar performance (KPNC model within IH data C-statistic: 0.734 [0.725-0.744]; IH model within KPNC data: 0.787 [0.768-0.805]). CONCLUSIONS: Across two large healthcare systems, intrasepsis factors improved postsepsis cardiovascular risk prediction as compared with presepsis cardiovascular risk profiles. Further exploration of sepsis factors that contribute to postsepsis cardiovascular events is warranted for improved mechanistic and predictive models.

Development of a healthcare system COVID Hotspotting Score in California: an observational study with prospective validation.

OBJECTIVE: To examine the value of health systems data as indicators of emerging COVID-19 activity. DESIGN: Observational study of health system indicators for the COVID Hotspotting Score (CHOTS) with prospective validation. SETTING AND PARTICIPANTS: An integrated healthcare delivery system in Northern California including 21 hospitals and 4.5 million members. MAIN OUTCOME MEASURES: The CHOTS incorporated 10 variables including four major (cough/cold calls, emails, new positive COVID-19 tests, COVID-19 hospital census) and six minor (COVID-19 calls, respiratory infection and COVID-19 routine and urgent visits, and respiratory viral testing) indicators assessed with change point detection and slope metrics. We quantified cross-correlations lagged by 7-42 days between CHOTS and standardised COVID-19 hospital census using observational data from 1 April to 31 May 2020 and two waves of prospective data through 21 March 2021. RESULTS: Through 30 September 2020, peak cross-correlation between CHOTS and COVID-19 hospital census occurred with a 28-day lag at 0.78; at 42 days, the correlation was 0.69. Lagged correlation between medical centre CHOTS and their COVID-19 census was highest at 42 days for one facility (0.63), at 35 days for nine facilities (0.52-0.73), at 28 days for eight facilities (0.28-0.74) and at 14 days for two facilities (0.73-0.78). The strongest correlation for individual indicators was 0.94 (COVID-19 census) and 0.90 (new positive COVID-19 tests) lagged 1-14 days and 0.83 for COVID-19 calls and urgent clinic visits lagged 14-28 days. Cross-correlation was similar (0.73) with a 35-day lag using prospective validation from 1 October 2020 to 21 March 2021. CONCLUSIONS: Passively collected health system indicators were strongly correlated with forthcoming COVID-19 hospital census up to 6 weeks before three successive COVID-19 waves. These tools could inform communities, health systems and public health officials to identify, prepare for and mitigate emerging COVID-19 activity.

Assessing the clinical impact of CYP2C9 pharmacogenetic variation on phenytoin prescribing practice and patient response in an integrated health system.

OBJECTIVE: To assess the impact of CYP2C9 variation on phenytoin patient response and clinician prescribing practice where genotype was unknown during treatment. METHODS: A retrospective analysis of Resource on Genetic Epidemiology Research on Adult Health and Aging cohort participants who filled a phenytoin prescription between 1996 and 2017. We used laboratory test results, medication dispensing records, and medical notes to identify associations of CYP2C9 genotype with phenytoin blood concentration, neurologic side effects, and medication dispensing patterns reflecting clinician prescribing practice and patient response. RESULTS: Among 993 participants, we identified 69% extensive, 20% high-intermediate, 10% low-intermediate, and 2% poor metabolizers based on CYP2C9 genotypes. Compared with extensive metabolizer genotype, low-intermediate/poor metabolizer genotype was associated with increased dose-adjusted phenytoin blood concentration [21.3 pg/mL, 95% confidence interval (CI): 13.6-29.0 pg/mL; P < 0.01] and increased risk of neurologic side effects (hazard ratio: 2.40, 95% CI: 1.24-4.64; P < 0.01). Decreased function CYP2C9 genotypes were associated with medication dispensing patterns indicating dose decrease, use of alternative anticonvulsants, and worse adherence, although these associations varied by treatment indication for phenytoin. CONCLUSION: CYP2C9 variation was associated with clinically meaningful differences in clinician prescribing practice and patient response, with potential implications for healthcare utilization and treatment efficacy.