Proportion and predictors of FVC decline in patients with interstitial lung disease.

RATIONALE: The proportion of patients who develop progressive pulmonary fibrosis (PPF), along with risk factors for progression remain poorly understood. OBJECTIVES: To examine factors associated with an increased risk of developing PPF among patients at a referral center. METHODS: We identified patients with a diagnosis of interstitial lung disease (ILD) seen within the Cleveland Clinic Health System. Utilizing a retrospective observational approach we estimated the risk of developing progression by diagnosis group and identified key clinical predictors using the FVC component of both the original progressive fibrotic interstitial lung disease (PFILD) and the proposed PPF (ATS) criteria. RESULTS: We identified 5934 patients with a diagnosis of ILD. The cumulative incidence of progression over the 24 months was similar when assessed with the PFILD and PPF criteria (33.1 % and 37.9 % respectively). Of those who met the ATS criteria, 9.5 % did not meet the PFILD criteria. Conversely, 4.3 % of patients who met PFILD thresholds did not achieve the 5 % absolute FVC decline criteria. Significant differences in the rate of progression were seen based on underlying diagnosis. Steroid therapy (HR 1.46, CI 1.31-1.62) was associated with an increased risk of progressive fibrosis by both PFILD and PPF criteria. CONCLUSION: Regardless of the definition used, the cumulative incidence of progressive disease is high in patients with ILD in the 24 months following diagnosis. Some differences are seen in the risk of progression when assessed by PFILD and PPF criteria. Further work is needed to identify modifiable risk factors for the development of progressive fibrosis.

Analysis of the Larissa Heart Failure Risk Score: Predictive Value in 9207 Patients Hospitalized for Heart Failure from a Single Center.

Early risk stratification is of outmost clinical importance in hospitalized patients with heart failure (HHF). We examined the predictive value of the Larissa Heart Failure Risk Score (LHFRS) in a large population of HHF patients from the Cleveland Clinic. A total of 13,309 admissions for heart failure (HF) from 9207 unique patients were extracted from the Cleveland Clinic's electronic health record system. For each admission, components of the 3-variable simple LHFRS were obtained, including hypertension history, myocardial infarction history, and red blood cell distribution width (RDW) ≥ 15%. The primary outcome was a HF readmission and/or all-cause mortality at one year, and the secondary outcome was all-cause mortality at one year of discharge. For both outcomes, all variables were statistically significant, and the Kaplan-Meier curves were well-separated and in a consistent order (Log-rank test p-value < 0.001). Higher LHFRS values were found to be strongly related to patients experiencing an event, showing a clear association of LHFRS with this study outcomes. The bootstrapped-validated area under the curve (AUC) for the logistic regression model for each outcome revealed a C-index of 0.64 both for the primary and secondary outcomes, respectively. LHFRS is a simple risk model and can be utilized as a basis for risk stratification in patients hospitalized for HF.

Meniscal Treatment as a Predictor of Worse Articular Cartilage Damage on MRI at 2 Years After ACL Reconstruction: The MOON Nested Cohort.

BACKGROUND: Patients undergoing anterior cruciate ligament reconstruction (ACLR) are at an increased risk for posttraumatic osteoarthritis (PTOA). While we have previously shown that meniscal treatment with ACLR predicts more radiographic PTOA at 2 to 3 years postoperatively, there are a limited number of similar studies that have assessed cartilage directly with magnetic resonance imaging (MRI). HYPOTHESIS: Meniscal repair or partial meniscectomy at the time of ACLR independently predicts more articular cartilage damage on 2- to 3-year postoperative MRI compared with a healthy meniscus or a stable untreated tear. STUDY DESIGN: Cohort study; Level of evidence, 2. METHODS: A consecutive series of patients undergoing ACLR from 1 site within the prospective, nested Multicenter Orthopaedic Outcomes Network (MOON) cohort underwent bilateral knee MRI at 2 to 3 years postoperatively. Patients were aged <36 years without previous knee injuries, were injured while playing sports, and had no history of concomitant ligament surgery or contralateral knee surgery. MRI scans were graded by a board-certified musculoskeletal radiologist using the modified MRI Osteoarthritis Knee Score (MOAKS). A proportional odds logistic regression model was built to predict a MOAKS-based cartilage damage score (CDS) relative to the contralateral control knee for each compartment as well as for the whole knee, pooled by meniscal treatment, while controlling for sex, age, body mass index, baseline Marx activity score, and baseline operative cartilage grade. For analysis, meniscal injuries surgically treated with partial meniscectomy or meniscal repair were grouped together. RESULTS: The cohort included 60 patients (32 female; median age, 18.7 years). Concomitant meniscal treatment at the time of index ACLR was performed in 17 medial menisci (13 meniscal repair and 4 partial meniscectomy) and 27 lateral menisci (3 meniscal repair and 24 partial meniscectomy). Articular cartilage damage was worse in the ipsilateral reconstructed knee (P < .001). A meniscal injury requiring surgical treatment with ACLR predicted a worse CDS for medial meniscal treatment (medial compartment CDS: P = .005; whole joint CDS: P < .001) and lateral meniscal treatment (lateral compartment CDS: P = .038; whole joint CDS: P = .863). Other predictors of a worse relative CDS included age for the medial compartment (P < .001), surgically observed articular cartilage damage for the patellofemoral compartment (P = .048), and body mass index (P = .007) and age (P = .020) for the whole joint. CONCLUSION: A meniscal injury requiring surgical treatment with partial meniscectomy or meniscal repair at the time of ACLR predicted worse articular cartilage damage on MRI at 2 to 3 years after surgery. Further research is required to differentiate between the effects of partial meniscectomy and meniscal repair.

Nomogram to Predict Risk of Postoperative Urinary Retention in Women Undergoing Pelvic Reconstructive Surgery.

OBJECTIVE: To develop a nomogram that determines an individual's risk of postoperative urinary retention (POUR) following pelvic floor reconstructive surgery. METHODS: We performed a retrospective chart review of women who underwent reconstructive surgery for pelvic organ prolapse and/or stress urinary incontinence. Short-term POUR was defined as failure of the trial of void (post-void residual >150 mL with a void of >200 mL) on postoperative day one or the need for re-catheterization in the first 2 postoperative days. Potential pre- and intraoperative risk factors for POUR were compared between patients with and without POUR. Multivariate binary logistic regression analysis with best-subsets variable selection was used to create a predictive nomogram. RESULTS: Most patients (275 of 332) had concomitant or combined procedures. The overall incidence of POUR was 31% (103 of 332 patients). The risk of POUR was higher for patients with high-grade anterior prolapse and those who had undergone anterior vaginal repair, vaginal hysterectomy, or a laparoscopic sling procedure. Patients who did not experience POUR tended to have fewer co-morbidities and were more likely to have undergone laparoscopic colposacropexy. Risk factors for POUR in the nomogram were diabetes, multiple medical co-morbidities, laparoscopic sling procedure, anterior vaginal repair, laparoscopic colposacropexy, and vaginal hysterectomy. The nomogram allows clinicians to calculate a patient's risk of POUR (range <10% to >80%). CONCLUSION: While the predictive nomogram in this study was developed using a single surgeon's case series and may not be generalizable to all surgeons, it demonstrates that the risk of POUR may be predicted based on clinical characteristics and the type of surgery performed. This kind of prediction model could help guide clinicians in preoperative patient counseling.

Natural Language Processing Improves Detection of Nonsevere Hypoglycemia in Medical Records Versus Coding Alone in Patients With Type 2 Diabetes but Does Not Improve Prediction of Severe Hypoglycemia Events: An Analysis Using the Electronic Medical Record in a Large Health System.

OBJECTIVE: To determine if natural language processing (NLP) improves detection of nonsevere hypoglycemia (NSH) in patients with type 2 diabetes and no NSH documentation by diagnosis codes and to measure if NLP detection improves the prediction of future severe hypoglycemia (SH). RESEARCH DESIGN AND METHODS: From 2005 to 2017, we identified NSH events by diagnosis codes and NLP. We then built an SH prediction model. RESULTS: There were 204,517 patients with type 2 diabetes and no diagnosis codes for NSH. Evidence of NSH was found in 7,035 (3.4%) of patients using NLP. We reviewed 1,200 of the NLP-detected NSH notes and confirmed 93% to have NSH. The SH prediction model (C-statistic 0.806) showed increased risk with NSH (hazard ratio 4.44; P < 0.001). However, the model with NLP did not improve SH prediction compared with diagnosis code-only NSH. CONCLUSIONS: Detection of NSH improved with NLP in patients with type 2 diabetes without improving SH prediction.